应用简化生物圈模式(SiB2)模拟通榆地气通量研究(英文)

主要通过对比观测与SiB2模拟的吉林通榆农田和草地样地的感热通量、潜热通量、CO2通量和气温来检测独立版简化生物圈模式的数值模拟能力。在模拟中,农田和草地代表半干旱区2种典型的地表覆盖类型,它们的全年冠层高度和叶面积指数都处于变化当中,在非生长季地表裸露,而在生长季,冠层高度农田可达2m,草地可达0.8m,对应的叶面积指数分别可达4.2和2.4。SiB2模型模拟用观测值为初始场,以半小时气象观测为驱动场。对2003年的模拟和观测的比对研究表明:在生长季模式具有很好的模拟小时时间尺度的潜热、感热和CO2通量及地表气温的能力;(2)模式模拟土壤热通量可能偏大;(3)模拟的逐日潜热、感热和CO2通量及地表气温可用合理地反映全年的季节变化。图11表3参23。

SiB2模型在黄灌区的应用探讨

SiB2(simple biosphere model Version 2)是用来模拟生态系统通量较为理想的国外模型,为了探讨其在我国黄河灌区的适用性及利用遥感数据驱动模型的可行性,并用其来研究该地区农田能量收支情况,以位山灌区为研究试点,利用位山实验站1a左右的观测数据对模型进行了验证分析,模拟结果表明:SiB2模型能够较好地模拟位山试验站农田的能量通量、CO2通量及地表温度,净辐射、潜热通量、感热通量、CO2通量与地表温度的模拟值与观测值吻合较好,线性相关系数R分别为0.988,0.714,0.607,0.677与0.933,其中净辐射模拟效果最好,感热通量偏差较大。另外,利用遥感MODIS LAI数据驱动SiB2模型表明,除净辐射外,模拟效果很差,因此在站点尺度遥感LAI(叶面积指数,leaf area index)产品不适合驱动SiB2模型。

Evaluating Soil Moisture Predictions Based on Ensemble Kalman Filter and SiB2 Model

Soil moisture is an important variable in the fields of hydrology, meteorology, and agriculture, and has been used for numerous applications and forecasts. Accurate soil moisture predictions on both a large scale and local scale for different soil depths are needed. In this study, a soil moisture assimilation and prediction based on the Ensemble Kalman Filter(EnKF) and Simple Biosphere Model(SiB2) have been performed in Meilin watershed, eastern China, to evaluate the initial state values with different assimilation frequencies and precipitation influences on soil moisture predictions. The assimilated results at the end of the assimilation period with different assimilation frequencies were set to be the initial values for the prediction period. The measured precipitation, randomly generated precipitation,and zero precipitation were used to force the land surface model in the prediction period. Ten cases were considered based on the initial value and precipitation. The results indicate that, for the summer prediction period with the deeper water table depth, the assimilation results with different assimilation frequencies influence soil moisture predictions significantly. The higher assimilation frequency gives better soil moisture predictions for a long lead-time. The soil moisture predictions are affected by precipitation within the prediction period. For a short lead-time, the soil moisture predictions are better for the case with precipitation, but for a long lead-time, they are better without precipitation. For the winter prediction period with a lower water table depth, there are better soil moisture predictions for the whole prediction period. Unlike the summer prediction period, the soil moisture predictions of winter prediction period are not significantly influenced by precipitation. Overall, it is shown that soil moisture assimilations improve its predictions.